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The Spatial Patterns of the Crime Rate in London and Its Socio-Economic Influence Factors

Author

Listed:
  • Yunqi Zhou

    (School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK)

  • Fengwei Wang

    (State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China)

  • Shijian Zhou

    (School of Software, Nanchang Hangkong University, Nanchang 330063, China)

Abstract

This paper analyses the spatial trends and patterns of the crime rates in London and explores how socio-economic characteristics affect crime rates with consideration of the geographic context across London. The 2015 London Crime Statistics and Socio-economic Characteristics datasets were used. First, we investigated the spatial patterns of crime rates through exploratory spatial analysis at the ward level. In addition, both the ordinary least square (OLS) model and geographically weighted regression (GWR) model, which allow the effects of factors to vary in spatial scales, were adopted and compared to explore the potential spatially varying effect across London. The results showed that there exists obvious spatial clustering for the crime rate in central London. Both global and local Moran’s I values indicated the spatial dependence of crime at the ward level. The GWR model performed better in explaining crime rates than the OLS model. Only two factors, namely, the percentage of children aged from 0 to 15 and employment rates, had significant spatial variability in London. The influences of the percentage of children aged 0 to 15 on crime rates are constantly negative over a spatial scale; however, employment rates positively affect crime rates in the north-western areas near the centre of London. Therefore, this paper focuses more on the spatial perspective, which fills the gap in traditional crime analysis, especially on the spatially varying influence of socio-economic status.

Suggested Citation

  • Yunqi Zhou & Fengwei Wang & Shijian Zhou, 2023. "The Spatial Patterns of the Crime Rate in London and Its Socio-Economic Influence Factors," Social Sciences, MDPI, vol. 12(6), pages 1-18, June.
  • Handle: RePEc:gam:jscscx:v:12:y:2023:i:6:p:340-:d:1167086
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